A constant modulus algorithm for blind equalization in α-stable noise

Channel noise is often assumed to be Gaussian in most of the existing channel equalization algorithms. The performance of these algorithms will degrade seriously when the noise is non-Gaussian. This paper deals with the problem of blind channel equalization in impulsive noise environment that is modeled as α-stable process. A modified adaptive error-constrained constant modulus algorithm (MAECCMA) is proposed by soft-limiting the amplitude of the equalizer input and transforming the error signal of the original adaptive error-constrained constant modulus algorithm (AECCMA) nonlinearly to suppress the influence of α-stable noise. Computer simulation results of two underwater acoustic channels show that, MAECCMA has almost the same performance as AECCMA and they both have faster convergence rate than constant modulus algorithm (CMA) and normalized least mean absolute deviation (NLMAD) algorithm in Gaussian noise, while MAECCMA provides the best performance of those four algorithms in α-stable noise.     

基于量子粒子群优化的正交小波加权多模盲均衡算法

针对常数模盲均衡算法(CMA)均衡高阶正交振幅调制信号(QAM)存在收敛速度慢、稳态误差大的缺点, 提出了基于量子粒子群优化的正交小波加权多模盲均衡算法(QPSO-WTWMMA). 该算法根据高阶QAM信号星座图分布特点, 将量子粒子群优化算法(QPSO) 和正交小波变换融入于加权多模盲均衡算法(WMMA)中. 因而, 利用QPSO对均衡器权向量进行了优化, 利用正交小波变换降低了输入信号的自相关性, 利用WMMA选择了合适的误差模型匹配QAM星座图. 理论分析及水声信道仿真结果表明, QPSO-WTWMMA算法可以获得更快的收敛速度和更低的稳态误差, 在水声通信中具有重要的参考价值.     

一种拖尾噪声环境中的稳健常数模盲均衡新算法

为了改善拖尾噪声环境中盲均衡器的性能,提出了一种稳健常数模盲均衡算法,修改了原常数模算法的误差函数,并进行非线性变换,有效地抑制了拖尾噪声的影响。采用浅海水声信道,在分别叠加高斯噪声与拖尾噪声的情况下,对算法进行了计算机仿真。结果表明:在高斯噪声环境中,新算法具有更快的收敛速度与更低的剩余码间干扰;在拖尾噪声环境中,原算法很不稳定,而新算法则性能稳健,能够稳定地收敛于较低的剩余码间干扰。     

迭代正则化方法求解电介质中空间电荷分布

首先介绍了迭代正则化方法的理论基础,建立了含有空间电荷密度分布的Fredholm第一类积分方程的反卷积算法,利用数值实验研究了加性高斯白噪声对迭代反卷积算法的影响,以及迭代停止标准对非适定问题的数值解的影响,最后使用该方法求解电介质样品中的空间电荷分布.结果表明,在无噪或者低噪环境下,反卷积算法能够非常好地计算出非适定问题的解.当噪声影响增大,信噪比降低时,反卷积的计算结果受到明显的影响.迭代停止标准对数值解的计算精度起着明显的作用.对实际测量数据进行处理表明,迭代正则化反卷积算法能够计算出固体电介质中的空间电荷分布.     

DWDM VSB modulation-format optical transmission: Effects of optical filtering and electrical equalization

The transmission of 40 Gb/s wavelength multiplexed channels under vestigial single side band modulation format is transmitted over long haul optically amplified fiber systems. Bit-error-rate (BER) of 10⁻¹² or better can be achieved across all channels. Optical filters are designed with asymmetric roll-off bands. Simulations of the transmission performance, BER versus receiver sensitivity are demonstrated with wavelength channel spacing of 20–40 GHz. An optical filter, whose passband is 28 GHz and 20 dB cut-off band, performs best for 40 Gb/s bit rate due to optimum filtering and minimum noise contribution. Furthermore the single-sideband property of VSB format can assist linear equalization by electronic processing. The transmission performance is accurately evaluated based on the eye opening using a fast statistical method based on an equivalent Gaussian probability density distribution (pdf) which is derived from multiple peaks pdf of distorted eye diagram.     

Improved Chernoff Bound of Gaussian Q-function with ABC algorithm and its QAM applications to DB SC and MRC systems over Beaulieu–Xie channels

In this study, we propose an improved upper bound for the Gaussian Q function by using artificial bee colony algorithm. Then, we investigate the performance of the dual-branch (DB) selection combining (SC) and maximal ratio combining (MRC) systems over the Beaulieu–Xie fading channels. The probability density functions of the instantaneous signal to noise ratio for the considered systems are obtained. Employing the proposed upper bound, we derive closed-form expressions of the error probability for the quadrature amplitude modulation (QAM) techniques such as rectangular QAM (RQAM), cross QAM (XQAM), and hexagonal QAM (HQAM). Furthermore, the asymptotic error probability expressions for the DB SC system are also obtained to simplify the analyses. The effects of some key parameters in the systems are shown in the results. Comparisons of the different modulation types and the different upper bound approaches for the Gaussian Q function are presented. Finally, it has been shown that the upper bound approximation presented in this study can be widely used for many communication applications.     

Non-linear function for a Gaussian photo-reception in standard IM/DD systems

In this paper, a set of mathematical transformations is developed to obtain the nonlinear function that recovers the Gaussian statistics after the square law distortion produced by the photodetector in Intensity Modulated/ Direct Detected IM/DD systems. Linear impairments in the optical domain like the Amplified Spontaneous Emission (ASE) or the Rayleigh Backscattering (RB) noise are considered the predominant. Here for the first time, a mathematical non-linear function is obtained without assuming any predefined function form. The result is that the noise distribution can be considered more signal-independent Gaussian after this non-linear transformation, than by using any other previously published operation. Applying this function in post-detection enables to make the system more linear, and then would improve the performance of the conventional electronic equalizers (EE). When using Maximum Likelihood Sequence Estimators (MLSE), a simplified metric can be used. Since the channel transfer function can be easily reproduced, computational cost and complexity are reduced.     

非线性混合模式的语音盲分离算法

本文针对FIR非线性混合模型，基于最大熵算法，提出了一种以高斯混合模式概率密度函数估计替代传统对数化概率密度估计的盲分离算法，以偶函数为非线性激活函数，采用最大期望（EM）迭代算法推导了分离算法的权向量迭代公式，通过模拟仿真实验结果与传统的最大熵和高阶累积量方法比较，新算法提高了收敛速度，并有效地完成了非线性语音分离任务，抑制了干扰语音信号的影响，提高了输出信噪比。     

天文图像多帧盲反卷积收敛性的增强方法

天文图像多帧盲反卷积的收敛性受到初始目标、约束条件和光子噪声等因素的影响.提出了用实际光学成像系统参数确定频率带宽有限约束的方法.用Knox-Thompson 方法重构初始目标相位形成盲反卷积算法的初始目标函数.研究了一种新颖的有效减小光子噪声、边缘效应和振铃现象的方法.根据最大似然估计理论，用期望最大化的优化方法建立了改进的严格约束多帧盲反卷积算法.模拟图像和实际天文图像的复原结果表明，所建立的多帧盲反卷积，可以有效克服大气湍流和减小光子噪声，改善天文观察图像的分辨率，并部分消除光学系统衍射效应对恢复图像的影响. 关键词： 大气光学 天文观测 图像处理和恢复     

Application of Maximum Entropy reconstruction to PISEMA spectra

Maximum Entropy reconstruction is applied to two-dimensional PISEMA spectra of stationary samples of peptide crystals and proteins in magnetically aligned virus particles and membrane bilayers. Improvements in signal-to-noise ratios were observed with minimal distortion of the spectra when Maximum Entropy reconstruction was applied to non-linearly sampled data in the indirect dimension. Maximum Entropy reconstruction was also applied in the direct dimension by selecting sub-sets of data from the free induction decays. Because the noise is uncorrelated in the spectra obtained by Maximum Entropy reconstruction of data with different non-linear sampling schedules, it is possible to improve the signal-to-noise ratios by co-addition of multiple spectra derived from one experimental data set. The combined application of Maximum Entropy to data in the indirect and direct dimensions has the potential to lead to substantial reductions in the total amount of experimental time required for acquisition of data in multidimensional NMR experiments.     

基于状态估计的混沌直扩信号联合均衡与解调

针对混沌直接序列扩频信号(以下称混沌直扩信号)通过非理想信道,利用信道和混沌直扩信号的状态方程及其均衡和解调的关联性,提出一种基于状态估计的联合均衡与解调算法.算法采用多扩展卡尔曼滤波(extended kalman filter,EKF)结构,一边对信道均衡,一边估计二进制信息码,二者互为因果,同时进行,不仅可以有效克服非理想信道所带来的多径信道畸变、加性噪声等对信号的影响,还可将掩藏在混沌直扩信号中的原始二进制信息码解调出来,比均衡和解调分离的做法更有效地利用信息,有更好的实时性.仿真结果表明,所提出的算法收敛速度快,能在信道特性未知情况下较好地抵御多径效应和噪声影响,实现了混沌直扩信号在非理想信道条件下的有效可靠传输. 关键词： 混沌直扩通信 非理想信道 联合均衡与解调算法 扩展卡尔曼滤波     

一种基于双分支改良编解码器的图像去噪算法

针对传统图像去噪算法多噪声去除难，深层卷积神经网络去噪模型网络复杂、训练时间长等问题，提出一种基于自编码器结构的双分支改良编解码网络，实现高效图像去噪。双分支结构之一采用降-升采样实现点噪声消除，另一分支专注于宏观的图像修复和伪像去除，后端利用残差结构进行整合，实现数字图像混合噪声去噪。实验结果显示:对于含有标准差为15，均值为0的高斯噪声、噪声密度为5%的椒盐噪声和散粒噪声的混合噪声图像测试集，实验去噪效果相较于输入混合噪声图像峰值信噪比，平均提升了5.3%。与12层全卷积神经网络相比，去噪效果相当，训练速度提升了25.4%，体现了其“轻量级”的优点。实验表明:该方法相较于深层卷积神经网络，训练速度快，网络简单；相较于传统图像去噪算法，噪声去除效果也较为明显。该算法可应用于轻量级视觉平台后端去噪。     

基于P范数的核最小对数绝对差自适应滤波算法

为了进一步提高在a稳定分布噪声背景下非线性自适应滤波算法的收敛速度,本文提出了一种新的基于p范数的核最小对数绝对差自适应滤波算法(kernel least logarithm absolute difference algorithm based on p-norm, P-KLLAD).该算法结合核最小对数绝对差算法和p范数,一方面利用最小对数绝对差准则保证了算法在a稳定分布噪声环境下良好的鲁棒性,另一方面在误差的绝对值上添加p范数,通过p范数和一个正常数a来控制算法的陡峭程度,从而提高该算法的收敛速度.在非线性系统辨识和Mackey-Glass混沌时间序列预测的仿真结果表明,本文算法在保证鲁棒性能的同时提高了收敛速度,并且在收敛速度和鲁棒性方面优于核最小均方误差算法、核分式低次幂算法、核最小对数绝对差算法和核最小平均p范数算法.     

Single voxel vascular transport functions of arteries,capillaries and veins; and the associated arterial input function in dynamic susceptibility contrast magnetic resonance brain perfusion imaging

PurposeThe composite vascular transport function of a brain voxel consists of one convolutional component for the arteries, one for the capillaries and one for the veins in the voxel of interest. Here, the goal is to find each of these three convolutional components and the associated arterial input function.Pharmacokinetic modellingThe single voxel vascular transport functions for arteries, capillaries and veins were all modelled as causal exponential functions. Each observed multipass tissue contrast function was as a first approximation modelled as the resulting parametric composite vascular transport function convolved with a nonparametric and voxel specific multipass arterial input function. Subsequently, the residue function was used in the true perfusion equation to optimize the three parameters of the exponential functions.Deconvolution methodsFor each voxel, the parameters of the three exponential functions were estimated by successive iterative blind deconvolutions using versions of the Lucy-Richardson algorithm. The final multipass arterial input function was then computed by nonblind deconvolution using the Lucy-Richardson algorithm and the estimated composite vascular transport function.ResultsSimulations showed that the algorithm worked. The estimated mean transit time of arteries, capillaries and veins of the simulated data agreed with the known input values. For real data, the estimated capillary mean transit times agreed with known values for this parameter. The nonparametric multipass arterial input functions were used to derive the associated map of the arrival time. The arrival time map of a healthy volunteer agreed with known arterial anatomy and physiology.ConclusionClinically important new voxelwise hemodynamic information for arteries, capillaries and veins separately can be estimated using multipass tissue contrast functions and the iterative blind Lucy-Richardson deconvolution algorithm.     

高速相干光通信系统中的自适应步长恒模算法

对于高速光通信系统中的相干检测来说,盲均衡是一项常用的恢复传输符号的技术。目前应用最广泛的盲均衡算法是恒模算法,但是一方面它不能兼顾收敛速度和收敛精度,另一方面在偏分复用系统中很容易陷入奇异性。因此提出了一种改进型的恒模算法———自适应步长恒模算法,并在112Gb/s偏分复用16进制正交幅度调制系统中对提出的算法进行了测试。与传统的恒模算法相比,改进后的算法收敛速度仅为恒模算法的1/20;收敛稳定后误差函数的方差比恒模算法降低了0.7dB;对偏分复用系统中的奇异性有很好的抵抗能力;在不考虑奇异性问题时,光信噪比代价与恒模算法相比降低了1.5dB。     

非高斯冲激干扰下基于Softplus函数的核自适应滤波算法

针对非高斯环境下一般自适应滤波算法性能严重下降问题,本文提出了一种基于Softplus函数的核分式低次幂自适应滤波算法(kernel fractional lower algorithm based on Softplus function,SP-KFLP),该算法将Softplus函数与核分式低次幂准则相结合,利用输出误差的非线性饱和特性通过随机梯度下降法更新权重.一方面利用Softplus函数的特点在保证了SP-KFLP算法具有良好的抗脉冲干扰性能的同时提高了其收敛速度;另一方面将低次幂误差的倒数作为权重向量更新公式的系数,利用误差突增使得权重向量不更新的方法来抵制冲激噪声,并对其均方收敛性进行了分析.在系统辨识环境下的仿真表明,该算法很好地兼顾了收敛速度和跟踪性能稳定误差的矛盾,在收敛速度和抗脉冲干扰鲁棒性方面优于核最小均方误差算法、核分式低次幂算法和S型核分式低次幂自适应滤波算法.     

语音通信降噪研究

语音通信系统中，语音通过信道传输将不可避免地引入码间串扰和信号畸变，同时受到噪声污染。本文在分析自适应盲均衡算法CMA(constant modulus algorithm）和改进盲均衡算法的基础上，考虑到自适应盲均衡技术在语音噪声控制方面能力有限，将自适应盲均衡技术与小波包掩蔽阈值降噪算法联合使用，形成一种基带语音增强新方法。仿真试验结果显示自适应盲均衡技术可以使星座图变得清晰而紧凑，有效减小误码率。研究证实该方法在语音信号ISI和畸变严重情况下，在白噪及有色噪声不同的噪声环境中都具有稳定的降噪能力，消噪同时可获得汉语普通话良好的听觉效果。     

时变带限信道中光通信的均衡与去噪技术

为了解决时变带限信道中的光通信问题,基于自适应均衡和盲均衡方法,提出了一种半盲光信号均衡方案,适用于大气海洋等时变带限光信道的光通信信号处理.对带限光信道中传输的包含泊松噪音开关键控调制光脉冲信号的不同数字均衡处理方法进行了数值模拟,比较了不同方法的结果,证明半盲均衡能更快匹配光信道且能保持收敛.同时,利用小波方法研究了含噪光接收信号去噪问题,发现光滑小波软阈值去噪方法更适合空间光脉冲信号波形,并对通信眼图进行了去噪的数值模拟.     

遗传优化神经网络的水声信道盲均衡

不需要训练序列的盲均衡技术可以有效地节省水声通信带宽，消除码间干扰，提高水声通信效率和质量。以前馈神经网络（FNN）作为盲均衡器，既适用于最小相位信道，也适用于非最小相位信道，包括非线性信道，但是前馈神经网络在实际的应用中其网络拓扑结构的选取和初始权重的确定缺乏理论依据，且其训练主要依靠BP算法，存在收敛速度慢、容易陷入局部极值及“过学习”的问题。为此，本文提出了一种遗传优化神经网络的水声信道盲均衡算法（GA—BP），对前馈神经网络拓扑结构和网络权重同时优化，有效地克服了传统前馈神经网络盲均衡的缺陷，提高了前馈神经网络盲均衡的泛化性能并加强了跟踪时变信道的能力和对信道突变的适应能力。水池试验结果证明了文中提出的遗传优化神经网络水声信道盲均衡算法的有效性，与直接前馈神经网络盲均衡相比较，均衡性能明显得到了提高。     

波导环境多目标时延-多普勒解卷成像

有源声呐感兴趣的参量是目标距离和径向速度,它们无法直接观测得到,需要通过估计而获得。利用波导多路径环境多目标时延-多普勒模型,可以导出采样互模糊度函数均值是发射信号自模糊度函数与广义目标反射性密度函数的两维卷积,其中广义目标反射性密度函数为信道扩展函数与目标反射性密度函数的两维卷积。依据信息理论最小Csiszar鉴别准则,可导出R-L (Richardson-Lucy)迭代解卷算法,对采样互模糊度函数均值进行两维迭代解卷积,消除发射信号和信道引入的模糊,序贯地实现时延-多普勒两维像的估计,进而获得多目标的时延和多普勒参量估计。仿真结果和海上实验数据分析验证了R-L解卷算法的可行性和有效性,较之常规的匹配滤波和维纳滤波算法,R-L算法有效地提高了时延和多普勒估计的分辨力和精度。